The present invention relates to the field of clinical anesthesia, in particular to the intraoperative and postoperative monitoring of patients' hypnotic and cognitive states.
The state of anesthesia is achieved by administering a combination of various anesthetic agents that render patients unconscious and insensitive to the trauma of surgery, while providing surgeons with a quiet surgical field. The concept of anesthesia, in the context of modern practice of balanced anesthesia, is a multi-component entity comprising hypnosis, analgesia and muscle relaxation. Thus the term “depth of anesthesia”, or “anesthetic depth”, is relevant for each of these components measured separately.
All general anesthetics lead to the loss of consciousness. At higher doses they also provide analgesia and muscle relaxation—two clinical end-points that can be independently achieved by analgesics and muscle relaxants. However, these drugs do not provide unconsciousness at clinical concentrations. Hence, although the mechanisms of anesthesia are still largely unknown, it is believed that hypnosis—i.e. drug-induced loss of consciousness and amnesia—is one of its major components.
Traditionally, anesthesiologists titrate drugs by assessing the anesthetic/hypnotic state of a patient based on observations of various clinical signs and their changes (such as blood pressure, heart rate, pupil dilatation, sweating, lacrimation, movement etc.). However, these signs may not always be readily available, and furthermore, may be unreliable. The need for a monitor of hypnosis is especially strengthened by the use of neuromuscular blockade agents in modern clinical practice. It is therefore possible for a patient to be aware of the surgery, yet unable to communicate his or her awareness by movement to the anesthesiologist. Therefore, a monitor of hypnosis/consciousness will provide anesthesiologists with a guide for the precise titration of anesthetic drugs, thus avoiding both overdosing and intraoperative awareness. It is expected that a better titration will result in fewer side effects, faster discharge from intensive care unit, and long term savings in terms of the drug quantities administered during surgeries.
All hypnotic drugs depress the Central Nervous System (CNS). Therefore it is natural to assume that Electroencephalographic (EEG) changes in the brain's electrical activity carry relevant information about drug effects on the brain. Thus, the hypnotic state of a patient could, theoretically at least, be quantified by observing variations in EEG waveforms.
Numerous studies have explored this field since the first observation of the effect of narcotics or general depressant drugs on the EEG in the late 1930's. However, the interpretation of the unprocessed or raw EEG signal is very complex, time consuming and requires an experienced specialist. Therefore, many efforts have been put into deriving EEG-based indices that correlate with the hypnotic state of a patient.
A number of inventions related to monitoring of anesthesia using electroencephalographic signals have already been disclosed.
In John, U.S. Pat. No. 4,557,270 issued Dec. 10, 1985 and U.S. Pat. No. 5,699,808 issued Dec. 23, 1997, systems monitoring patients in postoperative care units are disclosed. These prior art systems are based on Brainstem Auditory Evoked Potentials (BAER) and Brainstem Somatosensory Evoked Potentials (BSER) which are extracted from the EEG signals after an auditory or somatosensory stimulus has been delivered to the patient. The use of such signals suffers from a rather cumbersome setup and the heavy preprocessing of the EEG in order to extract the small evoked signals of interest from the background EEG. These systems also acquired other physiological quantities such as temperature, blood pressure, heart rate, etc.
Another invention using evoked potentials is described in John, U.S. Pat. No. 6,067,467 issued May 23, 2000. This invention further relies on the relative power in the theta band from 3.5 Hz to 7.5 Hz, which is used as an indication of blood flow and pain. A scoring algorithm is used to classify the patient's hypnotic state. The preferred embodiment for this invention is the closed-loop control of anesthetics drugs. A similar concept of closed-loop anesthesia using auditory evoked potentials is disclosed by Mantzaridis et al., international publication number WO 98/10701 published Mar. 19, 1998.
The use of time domain methods and frequency analysis to derive a number of parameters from spontaneous EEG has been thoroughly investigated. In Kangas, U.S. Pat. No. 5,775,330 issued Jul. 7, 1998, the inventor discloses such a technique to classify hypnotic states during clinical anesthesia. This prior art system further relies on a neural network to reach a single univariate descriptor. Maynard, U.S. Pat. No. 5,816,247 issued Oct. 6, 1998, also uses a similar analysis and a neural network for the classification of sleep states. Finally, in Schultz, U.S. Pat. No. 6,011,990 issued Jan. 4, 2000, an autoregressive model of the EEG supplements the spectral analysis. A multivariate classification function is further used to generate an appropriate index representative of the patient's hypnotic state.
Ennen, U.S. Pat. No. 6,317,627 issued Nov. 13, 2001, also uses spectral analysis. However, the disclosed invention uses additional observers that are further combined into a univariate index using component analysis.
Higher order spectral analysis has generated interest since the early 1990's. Chamoun, U.S. Pat. No. 5,320,109 issued Jun., 1994, and U.S. Pat. No. 5,458,117 issued Oct. 17, 1995, uses bispectral analysis combined with classical spectral analysis to derive an index of hypnosis. This invention's output is a weighted sum of different parameters that are mainly derived using spectral and higher order spectral analysis.
In Merilainen, WO 01/24691 published Sep. 30, 2000, the inventor discloses a system that measures the patient's brain activity by means of a light directed towards the patient's forehead. The light is filtered by the patient's tissues and the analysis of the resulting optical signal gives an indication of the patient's cerebral state.
Finally, in Vierto-Oja, WO 02/32305 published Apr. 25, 2002, the inventor uses the entropy of patients' EEG to ascertain their cerebral state. In one embodiment, the inventor combines a parameter obtained from spectral analysis of the Electromyogram (EMG) to provide a fast indication of change of the patient's state.
While spectral and higher order spectral analysis are the key techniques used in the prior art to provide an accurate and reliable index of hypnosis, clinical practice has shown that some delay exists between the change of the patient's anesthetic state and the changes in the indices that are available today. Although the disclosed techniques of Chamoun and Ennen are already being used with success in the operating room, an index reacting more quickly to changes in a patient's state is desirable. This is particularly true in the context of closed-loop anesthesia where a fast index will increase the stability of the system, hence allowing for better performance. Spectral and high order spectral analyses are particularly suited for signals with repetitive patterns. However, the electroencephalogram is typically a noise-like signal that does not exhibit observable patterns.
The use of auditory or somatosensory evoked potentials has also been thoroughly investigated by the research community. These potentials are particular patterns embedded in the electroencephalogram itself, and resulting from the external excitation of sensory functions. These patterns are clearly different whether the sensory information can be processed by cognitive functions (e.g., awake patient) or not (e.g., anesthetized patients). However, the analysis of these signals suffers from poor signal to noise ratio. Hence, considerable averaging is necessary to extract these potentials, which makes this technique unreliable in detecting rapid changes in patients' state.
Wavelets have generated great interest in the biomedical field. Their very low computational complexity associated with excellent joint time-frequency resolution properties makes them particularly well suited for the analysis of time-varying, non-stationary signals such as the EEG. Wavelets have been successfully used as a diagnostic tool to capture small-scale transients and events within the EEG, as well as to extract various features and waveform patterns from the EEG. Also, wavelets have been used in pre-processing of the EEG, when used as input signal to a neural network, and for the de-noising and compression of EEG data. However, no prior patent addresses the use of wavelet analysis in the context of spontaneous EEG analysis and diagnosing for clinical anesthesia.
Gillberg, international publication no. WO 00/69517, published Nov. 23, 2000 proposes the analysis of heart rhythms using wavelet analysis applied to electrocardiogram (ECG) signals. The digitized signals are analyzed by transforming them into wavelet coefficients by a wavelet transform. The higher amplitude coefficients are identified, selected and compared with pre-defined sets of wavelet coefficients, which are derived from signals of heart rhythms of known type. This method is used to discriminate normal from abnormal rhythms.
The object of the present invention is the use of wavelet analysis to extract a univariate feature from a spontaneous EEG signal that correlates to the patient's hypnotic state (referred to throughout this patent as WAVelet index), hence avoiding the complex and time consuming discriminant analysis and/or neural network training done in previous work.
Furthermore, previous systems are characterized by a significant time delay between the patient's true hypnotic state and the computed indices. This time delay is either the result of the analysis technique itself—such as in spectral and higher order spectral analysis—or the consequence of the large averaging needed in case of evoked potential analysis. Therefore, it is the object of the present invention to significantly reduce this time delay by using a different analysis technique applied to spontaneous EEG. This makes of the WAV a more precise feedback quantity for the monitoring, and/or manual/automatic control of anesthesia.
Finally, while previous systems rely on extensive tuning based on a large number of experimental data, it is an object of this invention to develop a method for diagnosing patients' hypnotic state which does not require neither a large subject pool, nor an extensive database of clinical EEG data.